Systems ecology can be understood as the application of systems theory to the study of ecology, as it studies the interaction between organisms and their abiotic environment through systems modeling. It is a holistic interdisciplinary science that crosses both physical and biological sciences but also economics and social sciences, to understand both ecosystems and also coupled socio-ecological systems in an integrated fashion.1 As a systems science, it is based upon the process of reasoning called synthesis that is focused primarily on the interaction between systems components and the patterns that emerge out of this instead of the properties of the components themselves. Some of the basic principles and theories within this area, including systems theory that provides the basic abstract generic models. Energetics that helps to study the flow of energy and materials through networks of biotic and abiotic elements within an ecosystem and the thermodynamic laws that govern these processes. Emergence and integrative levels of organization, that is used to structure our understanding of ecosystems in terms of emergent levels with their own integrated patterns and processes. Feedback loops are another set of models central to understanding regulatory processes and the dynamics of macro-scale complex ecosystems as they evolve over time.
The term ecology was introduced in the mid-19th century by the german zoologist Ernst Haeckel and it can be literally translated to mean the “study of the household of nature”.2 Haeckel intended it to be the study of the relationship between biological organisms, their interaction with each other and their physical environment. Ecology can be understood as the study of ecosystems, which are macro-scale systems of interacting biotic and abiotic elements, as such it is an interdisciplinary science that sits at the intersection of the physical and biological sciences including elements of biology, geography and earth science. Ecology is a relatively young science that has formed out of biology but more specifically botany and population dynamics. A major part of its formation during the 20th Century was in the study of energy and nutrient flows through ecosystems. In the later half of the 20th Century, the study was stimulated by the development of new tools and techniques including radioisotope tracers, computer science, and applied mathematics that enabled ecologists to label, track, and measure the movement of particular nutrients and energy through ecosystems. These modern methods enabled a new stage in the development of ecology called systems ecology.
Systems ecology is then the study of ecosystems that uses mathematical modeling, computation and, as the name implies, is based on systems theory. As with other areas of systems science, the use of systems theory as an approach involves the adoption of a holistic paradigm based on synthetic reasoning, meaning that systems ecology seeks a holistic view of the interactions and transactions within and between biological and geological systems on various scales. With this alternative approach, it does not restrict itself simply to the study of natural biophysical processes, but systems ecology now gives equal attention to the human dimension. Whereas standard ecology sees human industrial and economic activity as largely outside of its domain, systems ecologists recognize that the function of any ecosystem can be influenced by human economics in fundamental ways and that human industrial economic activity is a fundamental part of ecosystems around the world today. It has therefore taken an additional transdisciplinary step by including economics in the consideration of coupled socio-ecological systems. As such systems ecology takes an expansive domain of interest crossing almost all areas, from physics to biology, to economics and social studies to truly try and understand the workings of earth’s systems in all their multi-dimensional complexity.
A central part of systems ecology is the holistic paradigm derived from systems theory, which is in contrast to our more traditional approach taken within the natural sciences called analysis. Traditionally within modern science when looking at the macroscopic features of a given system, scientists have tried to find the origin of these phenomena by looking at the structure and properties of their component parts, by breaking the system down and then describing it as some linear combination of the parts, this process of reasoning is called analysis. Most of the success of modern science has relied on an analytical reductionist approach in which systems are taken apart to examine the individual components and how they interact together. Historical examples are the isolation and characterization of the elements of the periodic table and the discoveries of the particles that make up atoms. In the biological sciences, reductionism has also been very successful: examples range from the purification of proteins, DNA and RNA and the study of their structures and activities, to the sequencing and analysis of whole genomes.
Through analysis, we have developed a large and sophisticated body of knowledge within many specific domains, but this paradigm has also taken modern science down a particular trajectory. This is illustrated by one of the central figures in nonlinear science, the chemist Ilya Prigogine when he wrote In his autobiography: “This is indeed an essential part of the scientific revolution we are witnessing at the end of the 20th century. Science is a dialogue with nature. In the past, this dialogue has taken many forms. We feel that we are at the end of the period which started with Galileo, Copernicus, and Newton and culminated with the discovery of quantum mechanics and relativity. This was a glorious period but in spite of all its marvelous achievements it led to an oversimplified picture of nature, a picture witch neglected essential aspects. Classical science emphasized stability, order and equilibrium. Today we discover instabilities and fluctuation everywhere. Our view of nature is changing dramatically.”
In the past, the usual way to study complex phenomena was based on simplifying them through analytical reductionism, describing them as simple systems analogous to machines or by aggregating and averaging through statistical analysis, describing them as unorganized complexity. But complex systems, such as ecosystems, exist at a threshold between order and chaos because they are too complex to be treated as machines and too organized to be assumed random and averaged, they are best understood in terms of patterns and processes that emerge as we put the parts together.3 Simple systems may be governed by a single global rule that can be described in a beautifully compact equation, but complex systems are not governed by a single rule, they are what emerges out of the distributed interaction of many elements.
An ecology is what emerges out of the interaction of many different biotic and abiotic elements on different levels. where as with analysis we are breaking physical systems down to their most basic constituent elements, with systems ecology we are interested in what happens when we put things together, the processes that emerge on different levels as we build them up. Instead of talking about the properties of parts, we are talking about the connections between them. The fact that the properties of the individual units cannot always explain the whole has been known from the earliest times of science. In this context, it is often said that the whole is more than the sum of the parts, meaning that the global behavior exhibited by a given system will display different features from those associated with its individual components.4
A more appropriate statement would be that “the whole is something else than the sum of its parts” since in most cases completely different properties arise from the interactions among components.5 As an example, the properties of water that make a molecule so unique for life cannot be explained in terms of the separate properties of hydrogen and oxygen, even though we can understand them in detail from quantum mechanical principles. Some properties such as memory in the brain cannot be reduced to the understanding of single neurons . Life itself is a good example: nucleic acids, proteins, or lipids are not “alive” by themselves. It is the cooperation among different sets that actually creates a self-sustained, evolvable pattern called life.6
An example of this new approach to science is systems biology, which recognizes that through the analytical approach we have gained a very thorougher understanding of the component parts of biological systems. But that one of the great challenges in biology today is “putting it all together.” There is a large and rapidly growing body of information about the building blocks of cells, proteins, RNA, DNA, lipids etc. but how these molecules form organelles, and how cells form tissues and organisms, is far from understood, or equally how in developmental biology, the genome creates the organism? Self-organization plays a central role in all these processes and the answers are still largely a mystery. This is obviously not just an issue in biology on the micro level but also on the macro level in understanding ecosystems and just as importantly coupled socio-ecological systems.7
Systems ecology studies ecosystems through abstract mathematical models and computation. An ecosystem model is an abstract, usually mathematical, representation of an ecological system ranging in scale from an individual population, to an ecological community, or even the entire biosphere, which is studied to gain understanding of the real system. Systems theory is a formal modeling language, that is based on the model of a system, a system is a highly abstract model, in its essence it is simply a set of parts and relations between those part through which they are interdependent in effecting some joint outcome. This model is very effective in providing a generic language for talking about all kind of entities from a single cell to the entire Earth system. Systems ecology often deals with ecosystems on a higher level of abstraction than standard ecology, in order to be able to remove the details and derive formal models. These formal models of systems theory go hand in hand with computational methods that enable the interpretation of large amounts of data. This approach of using abstract models and computation allows us to approach understanding very complex ecosystems, such as the whole ecology of Earth in a formal fashion. Systems ecology is one of the few theoretical tools that can simultaneously examine a system from the level of individuals all the way up to the level of ecosystem dynamics. It is an especially valuable approach for investigating systems so large and complex that experiments are impossible, and even observations of the entire system are impractical.8
A second fundamental set of ideas within systems ecology is that of energetics, interpreting ecosystems in terms of the flow of energy through networks. Systems ecology studies the flow of energy and materials through networks of biotic and abiotic elements within ecosystems.9 It seeks to understand the processes which govern the stock and flow of material and energy and how they are processed through the system. Any ecosystem is characterized by flows: flows of nutrients and energy, flows of materials, and flows of information. It is such flows that provide the interconnections between parts, and transform the community from a random collection of species into an integrated whole, an ecosystem in which biotic and abiotic parts are interdependent.10 The analysis of how ecosystems function is determined by how those processes and components cycle, retain, process and exchange energy and nutrients. Systems ecology typically involves the application of computer models that track the flow of energy and materials and predict the responses of systems to perturbations. Ecosystems and biological systems in general challenge us because they are constantly consuming energy, and are therefore far from thermal equilibrium.11 Thus classical thermodynamics, which has been so successful in developing an understanding of physical and chemical properties such as temperature and pressure, does not apply to these systems. Instead of self-assembling into the lowest energy state, such as a crystal, these energy-dissipating components self-organize into highly dynamic structures, through which there is a constant flux of energy and material, and this is in many ways the defining feature of life. Within chemistry, this is called a dissipative system and the theory of dissipative systems goes a long way to helping us understand how biological systems self-organize and evolve over time into more complex organizations.12
Emergence & Hierarchy
The idea of hierarchy and integrative levels of organization is another major organizing theme within systems ecology. Integrative levels is an extension of the idea of emergence that addresses the biological organization of life that self-organizes into layers of emergent whole systems that function according to nonreducible properties.13 This means that higher order patterns of a whole functional system, such as an ecosystem, cannot be predicted or understood by a simple summation of the parts. These hierarchical structures have a nested pattern where smaller subunits are nested within larger subsystems and so on. Emergence gives ecosystems a distinctive omnipresent hierarchical structure and this scale hierarchy is a primary organizational principle, from biological cell to individual to community to ecosystem to biosphere. The study of ecosystems can cover 10 orders of magnitude, from microbes on the surface layers of rocks to the surface of the planet.14 In this hierarchy there are both processes and patterns that are universal, having a scale-invariant, fractal property as they recur on all levels, but also unique processes emerge on the different levels. This idea of synergies and self-organization leading to emergence and the formation of new levels in a hierarchical fashion is a central model for understanding the complex multi-dimensional characteristic of ecosystems.
Feedback Loops & Homeostasis
Another major modeling approach adopted from cybernetics and systems theory is that of feedback loops, which are central to understanding the dynamics of macro scale complex systems as they evolve over time and also to understanding processes of regulation and control within ecosystems and economy. On the micro-level feedback is well understood in the process of homeostasis, which means maintaining things at a steady state, negative feedback homeostasis is used in biochemical processes to regulate cells, individual organs and organisms. But macro processes of change, such as ecological succession are also regulated by feedback loops, as we go up from the organism to the community and the whole biosphere there is no homeostatic centralized control system, but now instead distributed feedback loops that work to stabilize the macro system into an oscillatory flow, bound within some upper and low limits. This process is called homeorhesis a term derived from the word “same” and “flow” as it refers to a stabilized flow.15
Feedback loops tell us a lot about the dynamics to ecosystems and biological systems in general, for example negative feedback regulates the human body as it grows. Starting with what is called a R stage of growth where most of the resources are used for development and little for maintenance, a period of high growth rate and positive feedback. Before at some stage reaching a mature state where negative feedback starts to limit the growth as the system enters what is called a k stage of growth, investing resources in other activities with negative feedback setting in as the system becomes more mature.16 Feedback loops are an example of nonlinear models that can be used to understand complex behavior within both ecosystems, economies and the interaction between them.17
Today there is a recognized need for sciences that cut across domains, in particular between the natural and social sciences. Our traditional scientific approach has provided us with great insight into many specific domains, but the 21st Century context requires us putting this domain specific knowledge together, to understand some of the most complex systems that involve the coupled interaction between society and ecosystem, and increasingly on the level of the whole biosphere. This requires complex system based models that are able to integrate the large amount of data now available to us and generate meaningful insight and analysis that is relevant to the challenges of sustainability. Bob Bishop the president of the International Centre for Earth Simulation18 describes this situation as such, “today we have… hundreds of satellites in space, earth observation satellites, high-performance computing and on the ground all kind of radar, we have data now coming in from all directions on our planet, about our planet, we have so much data we can’t use it all. We are not using it all, perhaps we are using only 20 percent of this data so far, and the other problem is that the data is specialized. So we do have a problem in ingesting so much data, we have a problem in analyzing it and we have a problem in connecting it across all the subsystems that are being measured. Integrate horizontally all this data from the hard sciences, the solid earth, the oceans the poles, the atmosphere on the one hand, and even the social sciences that drive our social economics on the other hand and the whole biosphere in-between. So we have so much specialized data, we have so much specialized science, that it is time to integrate it horizontally and understand what the entire planet is doing, the whole planet and have a holistic view of the planet not just a series of specialized opinions. Now I think the vertically specialized data and vision will always be there and I don’t think that will ever go away, but I am saying… that it is time to supplement and complement that with a full horizontal viewpoint, and the public is asking for it. I think society is saying that specialization is fine but give me the whole picture help me understand how these different specializations talk to each other and are interconnected, are coupled… this is a science that we don’t have and this is a science that we need to put into place.”